/*
 * Javlov - a Java toolkit for reinforcement learning with multi-agent support.
 * 
 * Copyright (c) 2009 Matthijs Snel
 * 
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package net.javlov;

import java.util.Arrays;
import java.util.List;

import net.javlov.util.ArrayUtil;

/**
 * Agent implementing the Q-Learning algorithm. Exactly the same as the Sarsa algorithm
 * (and agent) except that the target in Q-Learning is
 * 
 * {@code r + max_a' Q(s',a')} instead of {@code r + Q(s',a')}.
 * 
 * @author Matthijs Snel
 *
 */
public class QLearningAgent extends SarsaAgent {

	/**
	 * Default constructor that does nothing.
	 */
	public QLearningAgent() {}
	
	/**
	 * Constructs agent with given value function and gamma = 0.9.
	 * @param v the value function to use.
	 */
	public QLearningAgent(QFunction q) {
		this(q, 0.9);
	}
	
	/**
	 * Constructs agent with given value function and gamma.
	 * @param v the value function to use.
	 * @param gamma the discountfactor gamma e [0, 1].
	 */
	public QLearningAgent(QFunction q, double gamma) {
		super(q, gamma);
	}
	
	/**
	 * Only difference between Sarsa and Q-Learning is the target value towards which
	 * Q-values get updated.
	 */
	@Override
	protected <T> double getTDError(State<T> s, double reward, double[] qvalues) {
		return reward + gamma*getMaxVal(s, qvalues) - lastQValue;
	}
	
	@Override
	protected <T> double getSMDPTDError(State<T> s, double reward, double[] qvalues) {
		double err = optionAccumulatedReward + optionDiscount*getMaxVal(s, qvalues) - lastQValue;
		//System.out.println("R:" + optionAccumulatedReward + ", g^k:" + optionDiscount + ", last:" + lastQValue + ", err: " + err);
		return err;
	}
	
	//TODO can this be sped up, e.g. only storing allowed options in the q-table
	protected <T> double getMaxVal(State<T> s, double[] qvalues) {
		List<? extends Option> stateOptions = s.getOptionSet();
		double maxVal;
		if ( stateOptions == null || stateOptions.size() == qvalues.length )
			maxVal = ArrayUtil.max(qvalues);
		else {
			maxVal = Double.NEGATIVE_INFINITY;
			double val;
			for ( Option o : stateOptions ) {
				val = qvalues[o.getID()];
				if ( val > maxVal )
					maxVal = val;
			}
		}
		return maxVal;
	}
}